2.1 Motivations for the development of the systemic model
The Chinese government has recognized the problem of water scarcity. In December 2013, the guidelines from China’s Ministry of Water Resource called “water allocation plan for the development of coal bases”, suggest to plan the coal expansion accounting for the balance between water demand and resources on the sites. Planning and implementation of feasible measures in coal-based industry directed towards improving sustainable use of natural resources requires systems analysis approach to avoid potential negative effects of the inconsistent measures. In the following we present the basic version of the model that can be easily extended and adjusted to various situations. In this paper, in order to simplify the discussion, we avoid explicit treatment of inherent uncertainties and non-linear security (performance) indicators.
2.2 Outline of the model
The model has a rather general character and can be applied to investigate development prospects of multiple interdependent systems under restrictions on natural resource use.
In this work we present its pilot version focusing on the coal industry and investigating its competition with agricultural production for water and land resources in China. With a help of a scenario analysis the model evaluates resource, economic, and technical feasibility of plausible coal and agricultural demand trends in the presence of (energy, food, environmental) security goals and uncertainty about natural resources. Water and land act as limiting factors for the allocation of new and expansion of existing coal-related infrastructure.
We divide the coal-based industry into three stages: coal mining, processing and conversion. In our model a decision-maker minimizes the cost of the whole cycle of coal production from mining to processing, transportation and conversion, as well as the cost of producing agricultural commodities. Trading can be considered a conversion process.
Environmental considerations play an essential role in the choice of technologies. Seeking for lower cost under tight constraints on emissions and resource availability causes introduction of new and retrofitting of old coal mining, processing, and conversion technologies. The choice of different coal-related technologies depends on region-specific resource and demand constraints. For example, in a rich coal location, water scarcity can prevent implementation of the wet washing technology. In this case, the model evaluates the trade-off between the cost of dry washing, the cost of transportation and wet washing, and the no washing technology. In the latter case, the coal efficiency at the conversion stage will be lower while the emission of harmful pollutants will be higher.
So the central planner in her attempt to optimize the costs under the resource constraints takes advantage of spatial planning.
The structure of the model is presented in Fig.2. In the next section we proceed with the mathematical description of the model.
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2.3 Indices
The model accounts for various coal mining, processing and conversion technologies, as well as for different types of crops in a number of locations within the region under investigation. We consider the existing technologies as well as those, which are only at the beginning of implementation or even in the research stage, for example, various carbon capturing technologies. Index i is used to denote the type of coal, which is a combination of the coal class, the extraction (underground room-and pillar or long wall mining, surface strip or auger mining, etc.) and the processing (washing, cleaning, purification, enrichment) technologies, for example, lignite class, longwall mining, and wet washing. By t we denote a coal conversion technology resulting in end-use product (electricity, coke, heat, gasification and liquefaction). Import and export can also be considered as a way of conversion. Index k is used to represent different types of crops (corn, wheat, soybean etc.). Indexes j and m are used to denote different locations within the case study region. Depending on the chosen resolution, it refers to a county, a city, or a smaller geographical unit. Index d defines the end-use product such as electricity, gas, oil, coke, etc.
Fig.2: The structure of the model
2.4 Main Variables and goal function
In the model, variables xijmt denote the amount of coal (in tons) of type i produced in location j, transported to location m and utilized by technology t. Variables ykjm denote the amount of crop (in tons) k produced in location j and exported to location m. A social planner chooses how much of coal i and agricultural commodities k to produce in location l, so that the total cost of coal and agricultural production, transportation, convertion is minimized and constraints on natural resource utilization, environmnetal pollution, food security, energy (coal end-product) demand are fulfilled. The goal function is formulated as follows:
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, , , , , are costs associated with production of a unit (ton) agricultural commodity k in location
j, ckjmAT stands for the transportation cost of a unit of the agricultural commodity k from location j to location m,djm stands for distance(km) between location j and m By x and y we mean the sets of all xijmt and ykjm correspondingly.
2.5 Resource and security constraints 2.5.1 Land constraints
As mentioned in the introduction, in China about 40% of the total farmland area overlaps with coal reserves. The model incorporates two main farmland disturbances from coal mining - land subsidence and gangue (waste) deposits, both lead to land loss. A number of researches have done prediction of the land subsidence rate due to coal mining (Reddish and Whittaker, 2012; Donnelly et al., 2001; Xu et al., 2014). The character of the subsidence depends on the disposition of mined strata and also on the mining process in place (Chadwick et al., 1987). For example, if backfill mining is applied, the land subsidence can be prevented or controlled as voids are filled in with the low-cost solid materials, coming, e.g., from tailings. Using gangue for filling in the voids helps decrease the area occupied with the waste deposits. Subsided land can be recovered by reclamation programs; however it can take long time (Cong, 2013). Therefore, in our model we divide the farmland into three types: the land used for agriculture, subsided land and the land occupied by the gangue. We impose a land constraint prescribing that the total land used for agriculture, the land which subsides due to coal mining and the land occupied with waste deposits cannot exceed the total farmland in each location. Thus, the constraint is formulated as follows: coal filed in the location j, rijstands for the land reclamation rate (or efficiency rate) for coal i in location j. Coefficient g stands for the coefficient of the gangue1 occupied area resulting from the production of a unit of coal i in locationj (see Appendix for the details on the calculation of g).
1Gangue is left as a result of separating coal from other materials. It is important to separate gangue from lump coal before fed to thermal power plants.
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2.5.2 Food security constraints
We assume that the region under investigation aims to produce enough food to provide a required amount of calories (nutrition norms) to its population, i.e., ensure food security.
Domestic production can be supplemented by imports, however at higher costs. Thus we impose a constraint on the minimal required level of commodity k in location m as follows where the right-hand side DkmA defines the demand for agricultural commodity k in location m. This constraint requires accounting for transportation costs in the goal function (1) among locations having shortages and overproduction. Note that DkmA can be measured in terms of the minimum amount of daily calories per capita suggested by the World Health Organization (WHO) accounting for the size, age, sex, physical activity, climate, and other factors (Anderson, 2014).
2.5.3 Energy security constraints
Our model is driven by an exogenous demand for the final energy (electricity) converted from coal. Apart from electricity, the model includes the demand for such end-products of coal as heat, coke, gas, and oil. The demand scenarios at national or subnational levels can come from aggregate models. The conversion efficiency depends on the conversion technologies. Energy security constraint is responsible for fulfilling the demand for the end-products from coal. It is introduced as follows:
d 2.5.4 Water security constraints
Since water plays a key role in coal production and at the same time it is essential for agriculture and for the daily use of regional residents, we impose a constraint on total water consumed for coal extraction, processing and conversion as well as for crops irrigation in each location j:
j water availability in location j. Note that constraints on water use can be introduced in the model separately for coal production (mining) and coal conversion.
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2.5.5 Environmental security
The environmental security considerations are introduced in the form of emissions constraints, in particular, on emissions from coal conversion, of which SO2 and CO2 are the most important ones (Kreucher et al., 1998). In the model we include technologies, which are able to reduce SO2 and CO2 emissions, however, at a cost of additional water consumption.
SO2 is generated during the combustion of coal. The coal-based power plants are the main source of SO2 emission in China (Xu et al., 2012). In the future, due to the air emission standards to come into force, both new and existing coal-based plants will be required to install a Flue Gas Desulfurization (FGD) system in China. A wide range of commercial FGD processes are available to remove SO2 from the flue gas. By far, wet scrubbing system is the most common one with 80% of the global installed capacity. However the FGD systems require a lot of water and their introduction will increase water needs for coal-based power plants too (Carpenter, 2014). Thus, we impose a SO2 emission constraint associated with the coal conversion in location j, which sets up an upper limit for SO2 to be emitted in the location as follows:
Apart from SO2, coal-based power plants are the largest contributors to the atmospheric CO2 concentrations. According to the IEA estimates, CO2 resulting from coal-based power plants accounts for 45% of the total GHG emissions from fossil energy in China (PD, 2012). In order to decrease CO2, China needs to considerably reduce the coal demand and supplement coal mining with carbon capture and storage (CCS) technologies.
The timing and rate of this process will depend on the stringency of the near-term climate policy and will have important implications for the stranding of coal power plant capacity without CCS (Johnson et al., 2014). China will require commercial deployment of the CCS technology to begin in the next few years. The importance of CCS is expected to grow between 2020 and 2030 (WRI, 2010). However the CCS systems require additional cooling involving water. Introduction of the CCS systems, such as the wet cooling tower, doubles the water use at coal-based plants (Zhai and Rubina, 2011). Given that, the water pressure in coal-producing regions in China is expected to become even stronger.
In our model we impose a CO2 emission constraint associated with coal conversion by setting up an upper limit for CO2 emissions as follows:
2, 2
2.5.6 Coal productive capacity
The amount of coal produced in each location is constrained by the coal productive capacity of that location. Coal productive capacity is the maximum amount of coal that can be produced annually depending on geological conditions, mining technology and equipment. According to the Regulation of State Safety Work Administration, for the sake of safety, mining companies are forbidden to produce above the productive capacity which is registered in coal production license (SAWS, 2014). We impose the following constraint
2.5.7. Coal purification and enrichment processes: dry and wet cleaning Washing coal is a promising way of increasing its efficiency and utilization – it increases the coal quality as well as serves environmental protection. Washing helps remove the waste materials from coal. Also it makes the transportation cost lower. In China, the washing rate of raw coal is relatively low compared with, for example, the one in the USA and Australia. However, recently the Government has acknowledged the importance of washing coal in the energy development 12th Five-year Plan requiring the washing rate to increase up to 65% by 2015. In our model we impose a limit for the washing rate in each locationj as follows